Distributed computing platforms, such as Hadoop or other MapReduce-related frameworks, include software that allocates computing tasks across a group, or “cluster,” of distributed software components executed by a plurality of computing devices, enabling large workloads (e.g., data sets) to be processed in parallel and more quickly than is generally feasible with a single software instance or a single device. Such distributed computing platforms typically utilize a distributed file system that can support input/output-intensive distributed software components running on a large quantity (e.g., on the order of thousands) of computing devices to access a large quantity (e.g., petabytes) of data. For example, a data set to be analyzed by Hadoop may be stored within a Hadoop Distributed File System (HDFS) that is typically used in conjunction with Hadoop, which enables various computing devices running Hadoop software to simultaneously process different portions of the file.
Such distributed computing platforms may be deployed in a virtualized environment by provisioning many virtual machines (VM) for executing the distributed software components. However, provisioning so many virtual machines may be time-consuming and administratively prohibitive.